{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.import 必要工具包"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "数据集只有一个文件(diabetes.csv):Pima Indians Diabetes Dataset 包括根据医疗\n",
    "记录的比马印第安人 5 年内糖尿病的发病情况,这是一个两类分类问题。每个类的\n",
    "样本数目数量不均等。一共有 768 个样本,每个样本有 8 个输入变量和 1 个输出\n",
    "变量。缺失值通常用零值编码。\n",
    "Pregnancies: 怀孕次数\n",
    "Glucose: 口服葡萄糖耐受试验中,2 小时的血浆葡萄糖浓度。\n",
    "BloodPressure: 舒张压(mm Hg)\n",
    "SkinThickness: 三头肌皮肤褶层厚度(mm)\n",
    "Insulin:2 小时血清胰岛素含量(μU/ ml)\n",
    "BMI: 体重指数(体重,kg /(身高,m)^ 2)\n",
    "2) DiabetesPedigreeFunction: 糖尿病家族史\n",
    "3) Age: 年龄(岁)\n",
    "二、作业要求：\n",
    "    1. 对数据做数据探索分析（可参考0_EDA_ diabetes.ipynb，不计分）\n",
    "    2. 适当的特征工程（可参考1_FE_ diabetes.ipynb，不计分）\n",
    "    3. 采用5折交叉验证，分别用log似然损失和正确率，对Logistic回归模型的正则超参数调优。（各50分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.读数据+数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape (768, 9)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dpath='./home/listen/secondhomework/'\n",
    "train=pd.read_csv(\"/home/listen/secondhomework/TheSecondWeekHomework/diabetes.csv\")\n",
    "print(\"shape\",train.shape)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "9个特征768个样本，其中OUTcome为结果变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "存在缺失值：1.glucose=0;  2.bloodpressure=0; 3.skinthickness=0; 4.insuling=0; 5.BMI=0\n",
    "        ( 1、血浆葡萄糖浓度 2、舒张压          3、肱三头肌皮褶厚度 4、餐后血清胰岛素 5、体重指数)\n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3查看各类别数量关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Outcome']);\n",
    "plt.xlabel('Outcome');\n",
    "plt.ylabel('number');\n",
    "#不得病和得病数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig = plt.figure()\n",
    "sns.countplot(train['Pregnancies']);\n",
    "plt.xlabel('Pregnancies');\n",
    "plt.ylabel('target');\n",
    "#怀孕次数关系\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/listen/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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w59X5EHBcktVD1VVV76mqvf3kh+jOMxrUIutrMWcBb62qL1bVJ4Bb6f7vDl5bkgDPBt4yq+UvUtNivw+DfcdWYkDM5ZAeSdYCjwO29k2/0HcTLxt6U06vgPckuTbd2esAJ1bVbui+vMDDR6hrwbnc/z/s2OtrwWLraJ6+dz9F95fmgnVJPpLkH5J8/wj1tD67eVpf3w/cWVW3TLQNus72+30Y7Du2EgNiqiE9hpTk64C3Ay+pqs8DbwC+GXgssJuuezu0J1TVqXSj674gyZNGqKEp3YmUzwT+om+ah/V1MHPxvUtyEbAXeHPftBtYU1WPA14KXJ7k6wcsabHPbi7WV+853P+PkUHXWeP3YdFZG22HtM5WYkBMNaTHUJJ8Nd2H/+aqegdAVd1ZVV+qqi8Df8IMu9aLqao7+vu7gHf2Ndy50GXt7+8auq7emcB1VXVnX+Po62vCYuto9O9dko3AM4DnVr/Rut+E85n+8bV02/ofOVRNB/jsRl9fAEmOBn4UuGKhbch11vp9YMDv2EoMiLkZ0qPftnkpsKOqfm+ifXK74bOAG/d/7Yzr+tokD1l4TLeD80a69bSxn20jcNWQdU243190Y6+v/Sy2jq4Gzu+PNDkduGdhM8EQkjwNeBnwzKq6d6J9VbprsZDkFGA9cNuAdS322V0NnJvkQUnW9XX9y1B1TfhB4ONVtWuhYah1ttjvA0N+x2a9J34eb3R7+/+VLvkvGrGOJ9J1AT8KbO9vTwfeBNzQt18NrB64rlPojiC5HrhpYR0BDwO2ALf098ePsM4eDHwG+IaJtlHWF11I7Qb+l+6vtwsWW0d03f/X99+5G4ANA9d1K9326YXv2R/18/5Y/xlfD1wH/MjAdS362QEX9evrZuDMoT/Lvv2NwM/uN+8g6+wAvw+Dfcc8k1qS1LQSNzFJkqZgQEiSmgwISVKTASFJajIgJElNBoQ0IcmJSS5Pcls/zMg/J3lWkicn+aux65OGZEBIvf7EpL8E3l9Vp1TVd9OdSDn4wHbSPDAgpH2eAtxXVf8/vn9VfbKq/mBypv4aBr88MX1jP5gaSc7vB567Psmb+rZvSrKlb9+SZE3ffk7/2uuTvL9vOyrdtRs+3M//MzP/V0uLmKtrUksjewzdmbHLkuQxdGf/PqGq7k5yfP/UH9INw7w5yU8Bv083RPPLgadW1afTX8CH7uzie6rqe5I8CPinJO+pbshraVD2IKRFJHl9/9f9h6d8yVOAK6vqboCqWri+wPcCl/eP30Q3hALAPwFvTPLTdBdAgm7cq/PTXb1sK92wCusP7V8iLY89CGmfm+jG2QGgql6Q5ARg237z7eX+f1wd29+H6YZXXhhJ9WeTnAb8MLA9yWP793hhVb17ef8E6fCxByHt8/fAsUl+bqLtwY35bqe7PCX9dX/X9e1bgGcneVj/3MImpg/S7ewGeC7wgf75b66qrVX1cuBuuqGa3w38XD/MM0ke2Y+oKw3OHoTUq6pKcjbwmiS/CuwB/otumOxJb2ffZqAP040MTFXdlOS3gH9I8iXgI8DzgBcBlyX5lf49n9+/z6uSrKfrNWyhGx30o3TXPL6uP6pqD+Ne2lUrmKO5SpKa3MQkSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUtP/AXz3f6fgI3MYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#葡萄糖浓度数量\n",
    "sns.distplot(train.Glucose,kde=False);\n",
    "plt.xlabel('Glucose');\n",
    "plt.ylabel('number');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/listen/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome',y='Glucose',data=train,hue=\"Outcome\")\n",
    "plt.xlabel('outcome', fontsize=12)\n",
    "plt.ylabel('Glucose', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#血压关系\n",
    "\n",
    "sns.countplot(train.BloodPressure);\n",
    "plt.xlabel('BloodPressure');\n",
    "plt.ylabel('number');\n",
    "#血糖浓度为连续值分BOX更直观\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/listen/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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oFJGsSYjHmLhwqqWLhvYeinOmRpPYoHf7XWxIshlDqM1i3cAeEXlYRL47+BhrJxFZKyKHRKRCRO4eZnuSiDzpbN8iIqXO+mtFZLuI7HH+XhO0z8XO+gonjugew2nMMHZWtQBMuZrL4hmZZCYnWNOYGVOodyb6vfMImYh4gQeBa4FqYJuIbFDV/UHFbgeaVbVMRNYD9wOfAhqAj6tqjYgsAzYBRc4+PwDuADYDG4G1wB/GE5sxbtt5soXEBA/Ts5LdDmVcAte75LLZRoyZMYSUXFT1ERFJAWap6qEQj70SqFDVSgAReQJYBwQnl3XA153lp4HviYio6o6gMvuAZBFJAnKATFV92znmz4FPYMnFTDE7q1pYNjOTBM/UG1OzZl4uLx44Q01LFzOzp1bNy0yekP5ni8jHgZ3Ac87z5SKyYYzdioCqoOfVvFf7eF8ZVe0HWoHcIWX+HNihqj1O+eoxjmlMVBvwK/tqznJ+cbbboUzI6rk5gM0zZkYX6s+mrxOoibQAqOpOYM4Y+wzXF6LjKSMiSwk0lf3NOI45uO8dIlIuIuX19fVjhGrM5DnW0E5X3wDnFU3NMTKLp2eSleLjLet3MaMItc+lX1Vbh/SdD/ulHqQaKAl6XgzUjFCmWkQSgCygCUBEioFngFtV9WhQ+eIxjhkITvUh4CGAFStWjBWrMRHx2JaT71s3ePHkiaZOpmdOrT4XAI9HuKwslzeONKCqUT8vmnFHqDWXvSJyM+AVkfki8l/AW2Pssw2YLyJzRCQRWA8MbUrbANzmLN8AvKyqKiLZBAYQ3KOqbw4WVtVaoE1EVjujxG4Fng3xPRgTFWpauknwCPnpSW6HMmFXzM/n9NluKura3Q7FRKlQk8vfAkuBHuBx4CzwhdF2cPpQ7iIw0usA8JSq7hOR+0TkeqfYw0CuiFQAXwQGhyvfBZQBXxWRnc6jwNn2OeDHQAVwFOvMN1PMqZYuZmQl4/VM3V/8l5flAfD6kQaXIzHRKtTRYp3AV0Tk/sBTbQtxv40EhgsHr7s3aLkbuHGY/b4BfGOEY5YDy0J5fWOijV+VmpYulpdMzc78QSU5qczJS+P1I/V8+vKxul9NPAp1tNglIrIH2E3gYspdInJxZEMzJvY0d/TS0++PiSG8V8zPY3NlEz39A26HYqJQqM1iDwOfV9VSVS0F7gR+GrGojIlRp1oCc3IVxURyyaerb4B3TrS4HYqJQqEmlzZVfX3wiaq+AYTUNGaMeU9NSzdeEQoyp25n/qDVc3PweoTXj9hQf/N+oyYXEblIRC4CtorID0XkKhG5UkS+D7w6KREaE0NqWrsozEqaklfmD5WR7OOiWdnWqW+GNVaH/reGPP9a0LJdO2LMOKjTmb9kRqbboYTNFfPzeeDFwzS295A7hYdWm/AbNbmo6tWTFYgxsa61q4/O3oGY6MwfdOWCfP7zhcO8dqSeT15YPPYOJm6ENBTZuajxVqA0eB9V/bvIhGVM7KmJoc78QecVZZGblsirhyy5mD8V6vQvGwlMcb8H8EcuHGNi16mWbjzClJtmfzQej/CBBfm8eqiOAb9O6QtDTXiFmlySVfWLEY3EmBhX09JFfkYSPu/U78wPdtXCfJ7ZcYrd1S1cOGua2+GYKBHq//JHReQzIjJDRHIGHxGNzJgYU9PSxcys2GkSG3TF/HxE4NVDNiTZvCfU5NIL/F/gbWC78yiPVFDGxJqz3X209fRTNMVuaxyKnLRELijO5tXDllzMe0JNLl8Eypwr9Oc4j7mRDMyYWDLYmR+LNRcINI3trm6hsb3H7VBMlAi1z2Uf0BnJQIyJZTUtXQgwI4Y68+G9+9X09PlRhfufO8jykvf6XW5eNcut0IzLQk0uA8BOEXmFwLT7gA1FNiZUNS3d5KYnkeTzuh1KRBRNSyE10cvhM+1/klxM/Ao1ufzGeRhjJqCmpYtZualuhxExHhEWFGZw+EwbflU8dnfKuBfq/VweiXQgxsSqjp5+Wrr6WBNDF08OZ0FhOjurWjjV3EVJTuwmUhOaUK/QP8Ywc4lZp74xY6tpdTrzYzy5zC/IQIDDZ9osuZiQm8VWBC0nE7h7pF3nYkwIalq6gdgdKTYoLSmBomkpHD7TxgcXF7odjnFZqM1ijUNWfVtE3gDuHa68MeY9p1q6mJbqIyUxNjvzgy0ozOCVg3V09PSTlpTw7miykdhostgV6m2OLwp6rBCRzwIZEY7NmJhQ09IV801igxYWZqDAkbp2t0MxLgu1WexbvNfn0g8cJ9A0ZowZRVfvAE0dvayYHR/DcweHJB8508bykmy3wzEuCvUK/euAh4GXgDeBU8D6sXYSkbUickhEKkTk7mG2J4nIk872LSJS6qzPFZFXRKRdRL43ZJ9XnWPudB4FIb4HYyZdbZx05g/yiDC/IP3dIckmfoWaXH4DfBzoA9qdR8doO4iIF3iQQGJaAtwkIkuGFLsdaFbVMuAB4H5nfTfwVeBLIxz+FlVd7jzqQnwPxky6d6d9iZPkAoF+l47egXffu4lPoTaLFavq2nEeeyVQoaqVACLyBLAO2B9UZh3wdWf5aeB7IiKq2gG8ISJl43xNY6JKTWs3WSk+0pNC/ahNffML3xuSXDzNhiTHq1BrLm+JyHnjPHYRUBX0vNpZN2wZVe0HWoHcEI79U6dJ7KsidimwiV6nWrqYGWPziY0l/d0hydapH89CTS6XA9udvo7dIrJHRHaPsc9wX/pDG2FDKTPULap6HnCF8/irYV9c5A4RKReR8vp6mwrcTL7O3n4a2nriqkls0ILCDKqaOuns7Xc7FOOS8XTozwc+TKDv5WPO39FUAyVBz4uBmpHKiEgCkAU0jXZQVT3l/G0DHiPQ/DZcuYdUdYWqrsjPzx8jVGPC70DtWZT46m8ZtMCGJMe9kJKLqp4Y7jHGbtuA+SIyR0QSCYwu2zCkzAbgNmf5BuBl1ZGHmIhIgojkOcs+AklubyjvwZjJtqe6FYjP5FI8OEvy6Ta3QzEuiVgvo6r2i8hdwCbAC/xEVfeJyH1AuapuIDC8+VERqSBQY3l3eLOIHAcygUQR+QSBWtMJYJOTWLzAi8CPIvUejDkXe2vOkpaUQGZy/HTmD/KIUFaQzuG6dpslOU5F9H+9qm4ENg5Zd2/QcjcjXIypqqUjHPbicMVnTCTtPdVKUXYy8TrmZGFhBrurW6lt6Y7J2zub0YXa52KMGYfuvgGO1LXH/GSVo5lfGJgh6tAZaxqLR5ZcjImAfTWtDPg1rq/zSE9KoCg7MEuyiT+WXIyJgB0nWwAoyYnfmgvYkOR4ZsnFmAjYWdVCUXYKGck+t0Nx1cLCdBSosCHJcceSizERsONki80KDBTnpJLi81rTWByy5GJMmNW39XCqpcuSC84syYXpHD7TbrMkxxlLLsaE2c6qQH/LhbMsuUCg36W9p5/a1m63QzGTyJKLMWG2s6qZBI+wrCjL7VCiwvyCdABrGoszllyMCbOdVS0smpFBss/rdihRISPZx8ysZI5ap35cseRiTBgN+JVdVa3W3zLE3Px0TjZ10jfgdzsUM0ksuRgTRkfr22nv6Wd5yTS3Q4kq8/LT6PcrJ5s63Q7FTBJLLsaE0U7n4kmrufyp0tw0PII1jcURSy7GhNGWY03kpCUyNy/N7VCiSpLPS/G0VI7WW3KJF/E3F3iUeWzLyVG337xq1iRFYsJhy7FGVpbm4PHE50zIo5mXn8YfD9fT3Tdggx3igNVcjAmT6uZOqpu7WD03x+1QotLc/HT8CscbO9wOxUwCSy7GhMmWysAdulfNzXU5kug0KyeVBI9QWW/JJR5YcjEmTDZXNpKd6mOhcx8T86d8Xg+zcq3fJV5YcjEmTLYca7L+ljGU5adT29pNR49NwR/rLLkYEwY1LV2cbOq0JrExzM0PTAVT2WBNY7HOkosxYbDlWCMAq+ZYZ/5oirJTSErwUGlNYzHPkosxYbClsonM5AQWz8h0O5So5vUIpblpHLVO/ZgX0eQiImtF5JCIVIjI3cNsTxKRJ53tW0Sk1FmfKyKviEi7iHxvyD4Xi8geZ5/viog1cBtXqSpvHW1k5ZwcvNbfMqZ5+Wk0tPfQ2tXndigmgiKWXETECzwIXAcsAW4SkSVDit0ONKtqGfAAcL+zvhv4KvClYQ79A+AOYL7zWBv+6I0J3dH6Dk42dXLlwgK3Q5kS3u13saaxmBbJK/RXAhWqWgkgIk8A64D9QWXWAV93lp8GvicioqodwBsiUhZ8QBGZAWSq6tvO858DnwD+EMH3YcyoXjlYB8A1i2IvuYw1g8RETM9KJjXRa01jMS6SzWJFQFXQ82pn3bBlVLUfaAVGG25T5BxntGMaM6lePljHwsIMirJT3A5lSvCIMDcvjcr6dtRufRyzIplchmt8Hvo/KZQyEyovIneISLmIlNfX149ySGMm7mx3H9uON3F1DNZaImlufjotXX02BX8Mi2RyqQZKgp4XAzUjlRGRBCALaBrjmMVjHBMAVX1IVVeo6or8/Pxxhm5MaN440kC/X2OySSyS5jn9Lm9WNLociYmUSCaXbcB8EZkjIonAemDDkDIbgNuc5RuAl3WUerKq1gJtIrLaGSV2K/Bs+EM3JjQvH6wjK8XHRbPs/i3jkZeeSGZyAm8dbXA7FBMhEevQV9V+EbkL2AR4gZ+o6j4RuQ8oV9UNwMPAoyJSQaDGsn5wfxE5DmQCiSLyCeDDqrof+BzwMyCFQEe+deYbV/j9yquH6vjAgnwSvHbJ2HiICHPz03n7aCOqil1REHsiej8XVd0IbByy7t6g5W7gxhH2LR1hfTmwLHxRGjMxu6pbaGjv5ZpF1uw6EfPy09hZ1cLhM+0snG6TfcYa+7llzAT9dlctiV4P1ywqdDuUKWnwehdrGotNllyMmYABv/K73TVcuTCfrBSf2+FMSdNSE5mdm2qd+jHKkosxE7DlWCN1bT1cf8FMt0OZ0i6dl8uWykb6B/xuh2LCzJKLMRPw2101pCZ6+dBiaxI7F2vm5dHW08++mrNuh2LCLKId+sbEot5+Pxv3nObaJYU8s+OU2+FMaWuc+9+8dbSRC0psOHcssZqLMeP0+pF6Wrv6rEksDPIzklhQmG6d+jHIai7GjGK4iRsf33qSFJ+XUy1dJHjs99m5unReHk9sO0lP/wBJCV63wzFhYp8MY8ahvaef/bVnWT4r2xJLmFw6L5fuPj87T7a4HYoJI/t0GDMOO042M+BXLim12xmHy6q5uXgk0O9iYoclF2NCpKpsO97ErJxUpmcmux1OzMhK8bGsKIu3LbnEFEsuxoToWEMHDe29rLRaS9itmZfLjqpmOnv73Q7FhIklF2NCtPV4E8k+D+cVZ7kdSsy5dF4efQPKtuPNbodiwsSSizEhaHcu9FteMg2fzYAcdpeUTsPnFRuSHEPsU2JMCLYea2TAr6yea01ikZCamMCFJdOs3yWGWHIxZgz9fj9bKptYUJhOQYZ15EfKmnm57DnVSmtnn9uhmDCw5GLMGPZUt9LW08+l8/LcDiWmfWBBHqrw6uE6t0MxYWDJxZhRqCpvHW0kPz2J+QXpbocT05aXTCMvPYnn959xOxQTBpZcjBnFyaZOTrV0cWlZrt2KN8K8HuHaJQW8erCOnv4Bt8Mx58iSizGjeO1wPSk+LxeWTHM7lLjw4SXT6egdsKv1Y4AlF2NGcORMGwdOt7FmXi6JCfZRmQxr5uWSlujlBWsam/LsE2PMCH74WiU+r7x7zxETeck+L1ctLOCF/Wfw+9XtcMw5iGhyEZG1InJIRCpE5O5htieJyJPO9i0iUhq07R5n/SER+UjQ+uMiskdEdopIeSTjN/GrtrWLZ3eeYsXsHNKS7M4Uk+nDSwupb+thZ7XNkjyVRSy5iIgXeBC4DlgC3CQiS4YUux1oVtUy4AHgfmffJcB6YCmwFvi+c7xBV6vqclVdEan4TXx7+PVj+BUuL7Phx5PtqoUFJHiE5/aedjsUcw4iWXNZCVSoaqWq9gJPAOuGlFkHPOIsPw18UAJDctYBT6hqj6oeAyqc4xkTcS2dvTy29SQfP38G09IS3Q4n7mSl+LhqYQG/2XGK/gG/2+GYCYpkfb8IqAp6Xg2sGqmMqvaLSCuQ66zfPGRUb6k+AAAV90lEQVTfImdZgedFRIEfqupDEYjdxLFH3z5BZ+8Af3PlPHbYDawiarg7fQIUZiZR19bD6xUNXL2wYJKjMuEQyZrLcBcFDO2hG6nMaPtepqoXEWhuu1NEPjDsi4vcISLlIlJeX18fasyTormjlx+/Xsl133mdx7eepK3bpruIFt19A/zsreNctTCfxTMy3Q4nbi2cnkFqopeny6vdDsVMUCSTSzVQEvS8GKgZqYyIJABZQNNo+6rq4N864BlGaC5T1YdUdYWqrsjPzz/nNxMumysbWfPvL/GN3x8gwSMcqD3Ld146wt5TrW6HZoBflVfR2NHL566c53YocS3B4+GCkmxe2H+Gls5et8MxExDJ5LINmC8ic0QkkUAH/YYhZTYAtznLNwAvq6o669c7o8nmAPOBrSKSJiIZACKSBnwY2BvB9xBWzR29fOGJnczMSuG5L1zBb//2cu68uoyctEQe23qSE40dbocY1/oH/Dz0eiUXzspm5Ryb/dhtF8+aRu+An9/uGvqb1EwFEUsuqtoP3AVsAg4AT6nqPhG5T0Sud4o9DOSKSAXwReBuZ999wFPAfuA54E5VHQAKgTdEZBewFfi9qj4XqfcQTqrKl/9nN00dvXz3pgtZND3Q5FKYmcztl80hK8XHMztO0e+3Dky3bNhVQ1VTF5+9cp5N9RIFZmansHhGJk9Z09iUFNEB/Kq6Edg4ZN29QcvdwI0j7PtN4JtD1lUCF4Q/0sh7bOtJXth/hq9+bAnLiv70ToZJPi/XXzCTRzef4LXDDVyzyDowJ1v/gJ/vvnSExTMyuXZxodvhGMdNK0u499l9bD3WZLXJKcau0J8Enb39fOv5w6yZm8unLysdtsziGZksK8rilUN1NLT1TG6Ahmd31nC8sZMvfGg+Ho/VWqLFjReXkJuWyPdfrXA7FDNOllwmwaNvn6Cpo5cvfWThqM0tHzt/Bl6P8OJBm1dpMvUP+Pmvl4+wdGYmH15itZZokpLo5dOXz+HVQ/Xsq7FBL1OJJZcI6+zt56HXKrlifh4Xzx59Zt3MZB+r5+Swp7qVhnarvUyWZ3accmotC6yvJQr95erZpCcl8P1Xj7odihkHSy4R9svNJ2ns6OXvPzg/pPKXleXh9Qh/PBxd1+bEqu6+AR544TDnF2fxocXW1xWNslJ8/OXq2WzcU0tlfbvb4ZgQWXKJoO6+AX742lEuL8tjRWlonZEZyT4uKc1hx8lmmm18f8Q9/MYxalq7+aePLrZaSxS7/fI5pPq8/Mtv9xO4WsFEO0suEfT09moa2nu565qyce13xfw8BOE1q71EVEN7Dz949SjXLilktU2rH9XyM5L4hw8v5I+H69m4xya0nAosuUTIgF95+I1jXFCcxapxDqHMTk3kotnZbD/RTN3Z7ghFaL794mG6+ga4+7pFbodiQnDrmtksK8rkX367z6ZMmgIsuUTIiwfOcKyhg898YO6EmluuXFCAX5UfvV4ZgejM3lOtPL61iltWzWJefrrb4ZgQJHg9fPMT51Hf3sO//eGg2+GYMVhyiZAfvVZJ8bQU1i6dPqH9c9ISOb84m19sPklTh/W9hFPfgJ8vP72bnLRE/uHDC90Ox4zDBSXZ3HHFXB7bcnLEGZVNdLDkEgHbTzRTfqKZ2y+fQ4J34qf4ygX5dPcP8NM3j4UxOvPj14+xv/Ys/7puKVkpPrfDMeP0jx9ZyFUL87n32b28dbTB7XDMCOz+rRHw4CsVZKf6+IsVJWMXHkVhZjLXLZvOz948zv++Yq59EYZBZX07337xMGuXTmftshn26zfKjfTv84H5+eyrOcvnfvEOP/tfl3DhrNGvITOTz2ouYbarqoWXD9bxmSvmhuXe65+/qoy2nn4effv4OR8r3nX1DvD5X75DSqKX+9YtdTsccw6SfV5uW1NKZkoC6x/azHN7a90OyQxhySXMvvvSEbJSfNy6ZnZYjresKItrFhXw8BvH6OjpD8sx45Gq8pVn9nDoTBvfWX8hBZnJbodkzlFOWiLPfP4ylszM5HO/fId/+8MB+4xEEUsuYbS7uoWXDtbxmSvmkJEcviasO68uo7mzj8e3WhPORP1i8wl+veMUX/jgAq5cED03jzPnJi89icc/s5q/uLiEH/6xkmu+9SpPlVfR3Tfgdmhxz5JLGD3wwmGyUnzcdmlpWI978expXFaWyw9fq7QPzQQ8u/MUX9uwj2sWFfC347yg1US/ZJ+X+284n//53KUUZCTz5ad3c8k3XuSeX+/m97trqW3tcjvEuGQd+mGyad9pXjlUz93XLQprrWXQnVeXcfOPtvDktqqwJ69YtnFPLV98aheXlObw4M0X2XT6Mezi2dN49s7L2HyskafLq/nNjhoe31oFQIrPy7Q0H9NSE5mWmkh26nvLOWmJJCaM/Dv75lWzJustxBRLLmHQ1t3H157dx6LpGdx++ZyIvMaaubmsmZvLf2w6xIeWFFKUnRKR14kVqspjW0/ytWf3cWFJNj/560tISfS6HZaJMI9HuHReHpfOy+Pf/9zPAy8c5mRTJw3tPTR39lLX1sPhM230Dbw3P5kABZlJFE9LpawgnQUFGfZ/JQwsuYTBt54/zJm2bn7wlxfhO4frWkYjItz/5+ez9juv8eWnd/Hop1fZr/ARdPcN8LVn9/FkeRULCtP56HkzeHan3Yc9Fo01lLwkJ5WSnNQ/WaeqdPQO0NzR+27CqW7uZH/NWbafaEaAefnpXDx7GktmZkYw+thmyeUcvXa4nkfePs6tq2dHfKz9rNxU/vnPlvBPz+zh0c0nrHlsGG8dbeDeZ/dRUdfOXVeXMT0rGY/NdmyCiAjpSQmkJyX8SeLxq1Ld3MXB02fZVdXCk+VVJPs8HK1v5y9WlLzv9uRmdJZczsHu6hY++4vtLJqeyT+unZzJD29aWcLz+0/zzY0HmJufxhXzbeQTwIHas3zvlQp+v7uWkpwUfvq/LuHqhQV2kaQJmUeEWTmpzMpJ5UOLC6ms76D8RBNPbKvi52+fYFlRJusvmcW65TMj0q8aayQe7o2wYsUKLS8vD+sxK+vbufG/3yYl0cuvP3fphK+bGOvLb7jOxKaOXm7+0WYqGzr40a0r4nZobUdPPy8frOOp8ipeP9JAaqKX/335HD5/dRnJvkCbuSUXc64+et50nt1Zw+NbT3LwdBspPi8fO38G61fO4qJZ2TF9HyAR2a6qKyayb0RrLiKyFvgO4AV+rKr/PmR7EvBz4GKgEfiUqh53tt0D3A4MAH+nqptCOWakqSr/884p/mXDPnwJHn7+6ZWTfkFeTloij39mNbf8eAufeaScr358CTevnIU3CvtgJpI8h6Oq1Lf1UFHfzo6TLWw/0cybFQ309PspzEziHz+ykL9cNZusVPtFacIrOzWR2y4t5dY1s9lV3coTW0+yYVcNv9peTVF2CmuXTefqhQVcNDub1ERrDBoUsZqLiHiBw8C1QDWwDbhJVfcHlfk8cL6qflZE1gOfVNVPicgS4HFgJTATeBFY4Ow26jGHE46ai9+vvF3ZyMNvHOPlg3WsLM3hP268gFm5qWPvPIpz+fJt6ezlzsfe4c2KRpbOzOTLaxdx2bzcc5osczz6Bvw0tvdS39ZDQ0cPzR29NDmP5s5eGtt7OXSmja7eAfr9yoBf6R/wM6CKX8ErQlpSAj6v4PN6SPAKPo8HX4KQ4PHg8wpdfQOc7eqnob2Hzt73rvEpK0jnsnm5fPS8GawozRkxsVrNxZyr4T6D7T39bNxTy6a9p3n9SAO9A34SPMKyoiwWTc+grCCd4mmpFGYmkZOWSEqilxRf4DFZn89wOJeaSySTyxrg66r6Eef5PQCq+m9BZTY5Zd4WkQTgNJAP3B1cdrCcs9uoxxzORJPL9hPN7D3Vyr6aVt6saORUSxcZyQn87TVl3H753LDUFM71l72q8rvdtXzz9wc4fbabaak+rl5UwJIZmcwrSCc/PYnURC9pSQmkJHpJ9XlRAjcz6/crAwNKv9/PgF/p8ytdvf20dffT3tNPe3c/Z7v7aGjvpaG9J/C3rYf69h4a2nto6Rz+hk0egbTEBNKSEkhN9JKS6MXn9eD1CF6PkOARBBhQmJufRl+/n74BP31+pa/fT79fA88H/DR39JHs85CWlEBuehK5aYkUZ6eQGoZ524wJh+uXz6T8eBNbjjXxzolmjtS1j3qbDK9H3v1B5fN6KMhIItnnJdnnIcXnJTUxgYzkwYdvyN8EMp3lpARv4AeZ10Oi8/ny6/s/1/kZSRNuuovWZrEioCroeTWwaqQyqtovIq1ArrN+85B9i5zlsY4ZNl9+ehdH6zvISUvkguIsvrx2IR9ZOv3d9vxoICJ8/IKZXLukkFcP1fPc3lpeOVjHr985FdbXSU9KIC89kfyMJOYXpLNmbi556Ukca+hwRt4EElhaUgJJCZ6Q/zOPlTyt5mGiXXpSAlctLOCqhQXvrmts76G2tZu6tm427j5Nn99Pr/Mjqrf/vR9PfQN+CjOT6eoboKfPT0N7Lx29nbR199PW3Ud3n/+c4zv4r2td+c6KZHIZ7ttlaDVppDIjrR+uPjls1UtE7gDucJ62i8ihEeIc0wlgB/CziR5gdHnAiDeluCUyrzkRo8Y5URF4fxGJMwIszvByLc5x/h+e9DhT7p/wrnnAhGfgjWRyqQaCb2hSDAy9km2wTLXTLJYFNI2x71jHBEBVHwIemmjwk0VEyida7ZxMFmd4WZzhZXGGnxNr6UT3j2TP0jZgvojMEZFEYD2wYUiZDcBtzvINwMsa6ATaAKwXkSQRmQPMB7aGeExjjDEui1jNxelDuQvYRGDY8E9UdZ+I3AeUq+oG4GHgURGpIFBjWe/su09EngL2A/3Anao6ADDcMSP1HowxxkxMRIfcqOpGYOOQdfcGLXcDN46w7zeBb4ZyzCku6pvuHBZneFmc4WVxht85xRoXV+gbY4yZXFPnah5jjDFThiUXl4jIWhE5JCIVInK32/EMEpESEXlFRA6IyD4R+XtnfY6IvCAiR5y/kZ0COkQi4hWRHSLyO+f5HBHZ4sT5pDPww+0Ys0XkaRE56JzXNVF8Pv8/5999r4g8LiLJ0XBOReQnIlInInuD1g17DiXgu85na7eIXORynP/X+bffLSLPiEh20LZ7nDgPichH3IwzaNuXRERFJM95PqHzacnFBc7UOA8C1wFLgJucKW+iQT/wD6q6GFgN3OnEdjfwkqrOB15ynkeDvwcOBD2/H3jAibOZwPx0bvsO8JyqLgIuIBBv1J1PESkC/g5YoarLCAyaWU90nNOfAWuHrBvpHF5HYITpfALXuv1gkmKE4eN8AVimqucTmL7qHgDnc7UeWOrs833nu8GtOBGREgLTawVfvTyh82nJxR0rgQpVrVTVXuAJYJ3LMQGgqrWq+o6z3Ebgi7CIQHyPOMUeAT7hToTvEZFi4M+AHzvPBbgGeNop4nqcIpIJfIDAyEhUtVdVW4jC8+lIAFKc685SgVqi4Jyq6msERpQGG+kcrgN+rgGbgWwRmeFWnKr6vKr2O083E7g+bzDOJ1S1R1WPARUEvhtcidPxAPBl/vTi9AmdT0su7hhuapyiEcq6RkRKgQuBLUChqtZCIAEBBSPvOWm+TeCDMDhHRi7QEvRBjobzOheoB37qNN/9WETSiMLzqaqngP8g8Ku1FmgFthN953TQSOcwmj9fnwb+4CxHVZwicj1wSlV3Ddk0oTgtubgjlKlxXCUi6cD/AF9Q1bNuxzOUiHwMqFPV7cGrhynq9nlNAC4CfqCqFwIdREET2HCcPot1wBwCs5GnEWgSGcrtczqWaPx/gIh8hUCz8y8HVw1TzJU4RSQV+Apw73Cbh1k3ZpyWXNwRytQ4rhERH4HE8ktV/bWz+sxgVdj5W+dWfI7LgOtF5DiBZsVrCNRksp0mHYiO81oNVKvqFuf50wSSTbSdT4APAcdUtV5V+4BfA5cSfed00EjnMOo+XyJyG/Ax4BZ97/qPaIpzHoEfFbucz1Qx8I6ITGeCcVpycUfUTmPj9Fs8DBxQ1f8M2hQ8Vc9twLOTHVswVb1HVYuduY/WE5g66BbgFQJTCUF0xHkaqBKRhc6qDxKYeSKqzqfjJLBaRFKd/weDsUbVOQ0y0jncANzqjHJaDbQONp+5QQI3OPw/wPWq2hm0aaRpriadqu5R1QJVLXU+U9XARc7/34mdT1W1hwsP4KMERo4cBb7idjxBcV1OoMq7G9jpPD5KoD/jJeCI8zfH7ViDYr4K+J2zPJfAB7QC+BWQFAXxLQfKnXP6G2BatJ5P4F+Ag8Be4FEgKRrOKYGbB9YCfc4X3+0jnUMCzTgPOp+tPQRGv7kZZwWBPovBz9N/B5X/ihPnIeA6N+Mcsv04kHcu59Ou0DfGGBN21ixmjDEm7Cy5GGOMCTtLLsYYY8LOkosxxpiws+RijDEm7Cy5mLgjIgMislNEdonIOyJyqbO+dLhZYif4Gq+KyApn+biI7HFe73nnwjRjYpolFxOPulR1uapeQGCG2n+bhNe82nm9cuCfhm6cxNlwJ/W1TPyy5GLiXSaBaeT/hHMfk586NY4dInL1GOtTROQJ534XTwIpI7zea0CZs0+7iNwnIluANSJysYj8UUS2i8imoKlN/k5E9jvHfsJZd6VT+9rpxJEhIleJc18bp8z3ROSvneXjInKviLwB3Cgi80TkOee1XheRRWE6n8YAgUn1jIk3KSKyE0gGZhCYl2yoOwFU9Tzni/d5EVkwyvrPAZ2qer6InA+8M8Jrf4zAVc4QmBhyr6re68zn9kdgnarWi8ingG8SmEX3bmCOqvbIezea+hJwp6q+6Uwy2h3C++5W1csBROQl4LOqekREVgHfH+E8GDMhllxMPOpS1eUAIrIG+LmILBtS5nLgvwBU9aCInAAWjLL+A8B3nfW7RWT3kOO9IiIDBKaA+Wdn3QCBCUIBFgLLgBcC03rhJTA9B84+vxSR3xCYPgbgTeA/ReSXwK9VtdrZbzRPOu85ncCElL8K2idprJ2NGQ9LLiauqerbErida/6QTSN9U4/2DT7aXEpXq2rDkHXdqjoQdNx9qrpmmH3/jEDyuh74qogsVdV/F5HfE5j3bbOIfIjAdO7BTd3JQ47T4fz1ELhHy/JR4jXmnFifi4lrTtOWF2gcsuk14BanzAJgFoHJBUNZvww4f5yhHALynZoUIuITkaUi4gFKVPUVAjdGywbSRWSeBmayvZ/AIIFFwAlgiTPLbhaBWY3fRwP35zkmIjc6ryUicsE44zVmVFZzMfFosM8FAjWG21R1YEiz0veB/xaRPQRqBH/t9HmMtP4HBO42OTib9LimTlfVXhG5AfiukxgSCNyf5jDwC2edELiXfYuI/KszmGCAwLT4f3DieIpAM9oRYMcoL3kL8AMR+WfAR+CeOEPvQGjMhNmsyMYYY8LOmsWMMcaEnSUXY4wxYWfJxRhjTNhZcjHGGBN2llyMMcaEnSUXY4wxYWfJxRhjTNhZcjHGGBN2/w9SjZ3xddbyfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.BloodPressure,kde=True);\n",
    "plt.xlabel('BloodPressure');\n",
    "plt.ylabel('number');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/listen/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig=plt.figure()\n",
    "#sns.violinplot(x='Outcome',y='BloodPressure',data=train,hue='Outcome')\n",
    "#plt.xlabel('Outcome',fontsize=12)\n",
    "#plt.ylabel('bloodpresure',fontsize=12)\n",
    "sns.violinplot(x='Outcome', y='BloodPressure', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Outcome', fontsize=20)\n",
    "plt.ylabel('blood_pressure', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/listen/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'number')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#三头肌皮肤褶层厚度(mm)\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.SkinThickness, kde = False)\n",
    "plt.xlabel('SkinThickness')\n",
    "plt.ylabel('number')\n",
    "#疾病判断案例中，异常值可能就意味着得病，不能删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/listen/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='SkinThickness', data=train, hue='Outcome')\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('SkinThickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/listen/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'COUNT')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BIUnqMiAkSV0GhCSpy4CQJHUZEJKkLgNCktQ12A8Gqe9TN9+/qH6/95JnD1yJJC3MgNhPFvsPvyQdKCYSEEnuA34EPA7srKrpJMcAnwHWAfcBv1tVD0+iPknSZI8gXlZVD82bvhC4vqo+kOTCNv2eyZQ2eZ6KkjRpy+ki9QbgijZ+BfDaCdYiSSvepAKigP+e5NYkG1vbCVW1HaB9Hj+h2iRJTO4U0+lVtS3J8cB1Sb652AVboGwEePazPb0iSUOZyBFEVW1rnzuALwCnAg8mWQXQPnfsYdlNVTVdVdNTU1NLVbIkrThLHhBJnpbkyF3jwG8DdwCbgXNbt3OBq5e6NknSEyZxiukE4AtJdv39T1XVF5N8DbgqyXnA/cAbJ1CbJKlZ8oCoqnuBF3fa/w/w8qWuR5LU55PUK4DPVEjaGwbEGL5CQ9JKZUAc4AwwSUNZTk9SS5KWEQNCktRlQEiSugwISVKXASFJ6jIgJEld3uaqn/OBOknzeQQhSeoyICRJXQaEJKnLgJAkdXmRWk+aF7OllcGA0MQtJnAMG2npeYpJktTlEYQGsz9fRe5pLWnpLbsjiCRnJbk7ydYkF066HklaqZbVEUSSQ4D/BPwjYBb4WpLNVXXXZCuTlo5HS1oullVAAKcCW6vqXoAkVwIbAANCizKpX9ibxD/W+/u7Gjja3XILiNXAA/OmZ4GXTKgWadFW0k+/TuKus0kdVa2k79qz3AIinbb6hQ7JRmBjm3w0yd17+beOAx7ay2VXArfPwg667fPm/buuiWyf/fkdBv6b+7x99vG7/tpiOi23gJgF1s6bXgNsm9+hqjYBm/b1DyWZqarpfV3PwcrtszC3z8LcPgs7ULbPcruL6WvA+iQnJnkKcA6wecI1SdKKtKyOIKpqZ5K3A18CDgEur6o7J1yWJK1IyyogAKrqWuDaJfhT+3ya6iDn9lmY22dhbp+FHRDbJ1U1vpckacVZbtcgJEnLxIoMCF/nAUnWJrkxyZYkdya5oLUfk+S6JPe0z6Nbe5J8tG2z25OcMtlvMLwkhyT5epJr2vSJSW5u2+Yz7UYKkhzepre2+esmWfdSSXJUks8m+Wbbj17q/jOS5A/bf1d3JPl0kiMOxP1nxQXEvNd5vAp4AfCmJC+YbFUTsRN4d1U9HzgNOL9thwuB66tqPXB9m4bR9lrfho3ApUtf8pK7ANgyb/qDwCVt2zwMnNfazwMerqrnApe0fivBfwC+WFXPA17MaFut+P0nyWrgHcB0Vb2I0Q0353Ag7j9VtaIG4KXAl+ZNXwRcNOm6Jj0AVzN6B9bdwKrWtgq4u43/F+BN8/r/vN/BODB6Bud64EzgGkYPcT4EHLr7fsTorruXtvFDW79M+jsMvH2eAXx79+/p/lPwxBshjmn7wzXAKw/E/WfFHUHQf53H6gnVsiy0Q9qTgZuBE6pqO0D7PL51W2nb7SPAHwE/a9PHAj+oqp1tev73//m2afMfaf0PZs8B5oC/aKfh/jzJ03D/oaq+C3wIuB/Yzmh/uJUDcP9ZiQEx9nUeK0mSpwOfA95ZVT9cqGun7aDcbkl+B9hRVbfOb+50rUXMO1gdCpwCXFpVJwM/5onTST0rZhu16y4bgBOBZwFPY3SKbXfLfv9ZiQEx9nUeK0WSwxiFwyer6vOt+cEkq9r8VcCO1r6SttvpwGuS3Adcyeg000eAo5LsenZo/vf/+bZp858JfH8pC56AWWC2qm5u059lFBjuP/AK4NtVNVdV/w/4PPBbHID7z0oMCF/nweiuEuAyYEtVfXjerM3AuW38XEbXJna1v7XdjXIa8MiuUwkHm6q6qKrWVNU6RvvHDVX1ZuBG4A2t2+7bZtc2e0Prvyz+D3AoVfU94IEkf7c1vZzRa/lX/P7D6NTSaUl+tf13tmvbHHj7z6QvgkzoItLZwP8GvgX860nXM6Ft8A8ZHcbeDtzWhrMZnfu8HrinfR7T+ofR3V/fAr7B6A6NiX+PJdhOZwDXtPHnALcAW4G/Ag5v7Ue06a1t/nMmXfcSbZuTgJm2D/1X4Gj3n59vm/cD3wTuAP4SOPxA3H98klqS1LUSTzFJkhbBgJAkdRkQkqQuA0KS1GVASJK6DAhpD5I8up/Xty7JHW18OslH9+f6pf1t2f2inLQSVNUMo2cIpGXLIwhpjCRnJLlp3m8ffLI9IUuSDyS5q/3GwYda28eTvGHe8r90JNLWuet3Jt6X5PL2N+5N8o6l+m7SQjyCkBbnZOCFjN6f8z+B05PcBbwOeF5VVZKj9mH9zwNeBhwJ3J3k0hq9x0eaGI8gpMW5papmq+pnjF5Lsg74IfBT4M+TvB74yT6s/6+r6rGqeojRC+5O2NeCpX1lQEiL89i88ccZ/fDLTuBURm/EfS3wxTZ/J+2/rXYq6il7s/59LVjaVwaEtJfab2k8s6quBd7J6OV1APcB/6CNbwAOW/rqpH3n/6VIe+9I4OokRzB6W+kftvaPtfZbGL3R9McTqk/aJ77NVZLU5SkmSVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpC4DQpLUZUBIkrr+P8wwHjwn2GJSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#饭后2H胰岛素浓度关系\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Insulin, kde = False)\n",
    "plt.xlabel('Insulin')\n",
    "plt.ylabel('COUNT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/listen/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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eXh71YSMYiYVFfn5+mivyRluu4A6z/xlmdQV3EigsRDJfXl4ewWprutZC11l81tB9nvcDbgCeTV45nVvzZijnHNaZprQUaScCgQBhB2H3yfPOoNVXcDvnNu7zsxj4NvCz1u7DzHxm9raZzYo/H2pmb5rZB2b2qJllx5fnxJ+vj78+pG1vq31qvLEK6CxDJFP5/QEi0U9mnvX5OkfDyqFO99EF6NWG9a8H1jR7fisw1Tk3EtgNXBlffiWw2zk3ApgaX6/Dax4QCguRzBR1Ucyg8by/s8y80OqwMLN/mtm0Zj+PA8uAf7Vy+xLgq8SvBLdYG8sXgcfjqzwEnBd/fG78OfHXJ1onaJNp3gylkVEimSkSjpBlkBX/ROosf6tt6bNYv8/zGuCvzrlXWrn9n4CfAo2DknsAe5xzjV+hy4AB8ccDgE0Azrmwme2Nr7+rDfW2O83/0enMQiQzBevr6OZzZPtiZxTBYDDNFXmjLVOU/+ZgD2JmZwM7nHPLzOwLjYtbOkwrXmu+36uAqwAGDRp0sOVljOZhoQv0RDJTXV0dZXU+QpHYvS1qa2vTXJE32tIMdZGZHRF/PMrMXjOzuWZ2eCs2PwWYbGYbgBnEmp/+BHQ1s8bAKgG2xB+XAQPjx/IDxUDFvjt1zv3dOTfOOTeuV6+2dJ1kJjVDiWS+yqoq6iNGRTDWsV1ZWZnmirzRlg7um/jkA/sPwFvAfOAviTZ0zv3cOVfinBsCXAjMdc5dDLwKTImv9m3g6fjjZ+LPib8+13WCXiSFhUhmq6urIxhqwGfgs9hH0p49e9JclTfa0mfRyzm33cxygQnEPsQbOLR+hJ8BM8zsJuBt4L748vuAf5rZemIBdeEhHKPdUDOUSGbbtSv2cefPcgSyPr2so2tLWOw0sxHAGOAt51zQzPJpuX9hv5xz84B58ccfAZ9rYZ164Py27LcjUAe3SGbbsWMHAIGs2GioPL81Levo2hIWvyU2VDYCfDO+bCLwTrKL6qxCoVDT4+YX6IlIZtiyJdatGsiKNUH1zIs0Levo2jIa6kEzmxl/3Nj9/yadpInIC83DovljEckMW7ZswZ8F/ngTVJ/cBjaXlaa3KI+05cwC51ytmfU2s76pKqgzaz5eu76+Po2ViEhLNm7cSN9819T23i8/wttl2wiHw/j9bfo4bXfaMnT2TDPbDGwjdoFe488HKaqt06mrq2u6mKSuri6ttYjIZ23c8DH98j456++XHyESibJ58+Y0VuWNtgyd/TOxfosC51xWs5/OMYuWB6qqq3HZsRup1NTUpLkaEWkuGAyydes2BhR8MhClpDD2+OOPP05XWZ5pS1h0A/7mnNNX3hSpqqoiml0IQHV1dZqrEZHmNmzYQNS5poAA6J8fwUxhsa/7gMtTVYhAdVU1LicWFlVVVWmuRkSa+/DDDwEYVPjJsPZsH/QrcKxfv+/UeR1PW3pkTgSuN7MbiPVbNHHOnZbUqjqp6upqXI/+mC+gsBDJMOvXryfHD73zPn3B7KD8EB+8vy5NVXmnLWFxb/xHUiAYDNLQEML5cyCQo7AQyTDvr1vH4MJw09TkjQYXhVn84S727t1LcXFxeorzQMKwMLMvxh9uSnEtndrevXsBcL4cor6cTjM5mUh7EIlEWL/+A07t9dmLZYcUxZql3n//fcaPH+91aZ5pzZnFfQled8CwJNTSqTWGg/PnEsnKZk88PEQk/UpLS6kPhhhW9NlpeIYUxTq8161b17nDwjk31ItCOrtPwiIH589hzx6FhUimWLt2LQBDunx2NuiCgKNvgWtap6M61HtwS5J8Oixy1QwlkkHee+898gNGv/yWbx0wrDDEe6vf7dD341ZYZIimPgt/Ls6fQ011laYpF8kQq1e9y9DC0Gc6txuNKG6gYvdetm/f7m1hHlJYZIjG0U/Onx07u3BOV3GLZIDq6mo+3rCRUV33PxP0yOJYX8a7777rVVmeU1hkiMrKSswXgCx/bPgsujBPJBOsWrUK5xyHFe8/LAYWRsgLwMqVKz2szFsKiwxRXV0N/tgN4J0vFhbqtxBJv+XLlxPIghHF+78hWZbB4cUhli97y8PKvKWwyBDV1dU4Xyws8AWalolIei1dsoQRxQ1kJ5gydXS3BjZv2cbWrVu9KcxjCosMUVNTQyQrNpK5MTQ0TblIem3bto2PNmzg2B6Jb0Z2THydRYsWpbqstFBYZIiamlpcVuyMwsXPLNTBLZJeCxYsAGhVWPTJj9K/wPHGG6+nuqy0UFhkiOqams80Q9XW1h5gCxFJtTlzXmFgUZR+Ba0bxj6+Vx0r3l5BeXl5iivznsIiQ9TU1JBVt4ec0sVNoaEzC5H02bx5M++9t4YTe7W+OfjEPiGizjF37twUVpYeCosMUV1dhUVCZNVWQJZP05SLpNns2bMxg1P6JW6CajSgIMLw4giznn2mw13NrbDIADU1NTSEQmDNhltk51FRUZG+okQ6sWAwyHOznuXYHiG657RtJoUv9KtjY+kmVqxYkaLq0kNhkQEapwhwWZ/87wj789i2reNOHSCSyV5++WX2VFbxlZK2j0g8qU+QomyY+eijKagsfRQWGaC0tDT2IOuTSYAjOcVsbFwuIp4Jh8M88vDDDOkS5Yhu+78Qb3+yffClAbUsWry46VasHYHCIgOsX78ezHDNwiKa342qyr3s3LkzjZWJdD5z5sxh85YtTB5cg+1n4sBEziipJy8ADz30UHKLSyOFRQZ4991VRPN70PxfZqSgNxCbl0ZEvNHQ0MCDD9zP4KIox/dsfcf2vgoDjq8MqGX+/Pm8//77SawwfRQWaVZbW8uq1asIF/b91PJofg/Mn83SpUvTVJlI5/Pss8+yddt2pgyrPuizikZnDqqnMBv+9te/Jqe4NPMkLMxsoJm9amZrzGy1mV0fX97dzF42sw/iv7vFl5uZ3Wlm681spZmN9aLOdFi2bBmRcJhw15JPv5CVRaioPwsXLdJ9LUQ8UFNTw7QHH+CIbmGO7r7/GWZbK9/vmDy4hmXLl7NkyZIkVJheXp1ZhIGfOOeOAE4ErjWzI4EbgDnOuZHAnPhzgLOAkfGfq4B7PKrTc/Pnz8cCOUT2ObMACHcdxO6KCtasWZOGykQ6l+nTp7OnsooLhx/6WUWjiQPq6Z3vuOcvfyYSafkue+2FJ2HhnNvqnFsef1wFrAEGAOcCjT1ADwHnxR+fC0xzMYuBrmbWz4tavRQMBnnjjQWEigdB1mf/V4S7DoIsH6+++moaqhPpPLZv385jj83k5D5BhrZwn+2DFciCC4bFbp70/PPPJ22/6eB5n4WZDQGOA94E+jjntkIsUIDe8dUGAJuabVYWX9ahLFmyhLq6Whq6D215BX82DV0GMGfuq2qKEkmh+++/HyJhpgxP/nxs43uFGF4c4YH772vXM0l7GhZmVgg8AfzQOXegO/u0dBL4mWvnzewqM1tqZkvb4xDTV199FQvkEinqv991wt2HsruiXKOiRFLk448/5qWXXuSMkjp65ib/S5kZXDi8mvKK3Tz55JNJ379XPAsLMwsQC4rpzrnG/2LbG5uX4r93xJeXAQObbV4CbNl3n865vzvnxjnnxvXq1St1xadAXV0db7yxgGDXIS02QTUKdx2E+QLMmTPHu+JEOpEHH3yQXB98dXDqvvUf1jXM0T0aeHTGI+12NmmvRkMZcB+wxjn3x2YvPQN8O/7428DTzZZfGh8VdSKwt7G5qqN48803CYWChPfXBNXIFyDUpYRX573W7jvIRDLNpk2beO211/jSgFqKAqmd+O9rQ2uprKrm2WefTelxUsWrM4tTgEuAL5rZivjPJOAW4Awz+wA4I/4c4DngI2A98A/gex7V6ZlXX30Vy84jUtQn4brh7kOo3LuHd955x4PKRDqPp556Cl8WfHlgfcqPNbxLmMO6hvn3k0+0yy9+/sSrHDrn3Bu03A8BMLGF9R1wbUqLSqO6ujoWLlpEsOswsMR5HS4eiPn8zJs3j7FjO+wlJyKeCoVCvPD885zQK0hxduvPKv71fj4bq2IzRN+8vAuDCsN8a1TrmpbOKKnj7lU7WL58OePHjz+outNFV3CnwcKFC2kIhQh3S9AE1cjnJ1Q8kFdfnUc43PaJzUTks5YtW0ZtXR0n9Q22abvSaj91kSzqIlms3ROgtLr137mP7REi12/Mnz+/reWmncIiDV566SXIKSRS9NkL8fYn3H0YVVWVHeJKUJFMsHjxYvL8xuhuh361dmtl++Do7vUsXPCGZ8dMFoWFx3bs2MGSJUsIdh9OWy4TDReXYNl5zJ49O4XViXQe69atZUhRCL/Hn4IjisOUV+xud/fpVlh47N///jcOaOg1qm0bZvmo7z6ShQsXsnVrhxoYJpIWH3/8MQMLvG/WHVwYbjp+e6Kw8FBVVRVP/fvfNHQdjMspavP2DX2OxJkxY8aMFFQn0nkEg0GCwRDF2d7PjNAl3pleVVXl+bEPhcLCQzNmzKCutpZQv2MOanuXnU+ox0hmzZrF5s2bk1ydSOfROO1Gji/BiimQ44uFRXu7OE9h4ZGtW7cy87HHaOg+jGhBj4PeT6j/cUQx/tpB5sgXSYe8vDwAgmm43CEYifVV5ufne3/wQ6Cw8IBzjjvuuINwxBEsGXdo+8rOp77v0bz++ussXrw4SRWKdC45OTnkZAeobPD+I7AqFAuLoqK2N0Wnk8LCA3PnzmXx4sXU9TsWl1N4yPsL9R0DeV25/Q9/oKamJgkVinQ+/fv3Z0ed9+1Q2+PH7N9//xOIZiKFRYpVVFQw9U9/IlrYi4a+o5Oz0ywfNUMmsGvXLu65p8PeF0okpQYOGszWuoDnx91S6yMQ8NOnT+KpfjKJwiKFnHPcdttt1NTUUjfk1FZN7dFa0cLehPocxaxZs3jzzTeTtl+RzmLo0KFsrzHP+y3Kqn0MHjwYny8NveuHQGGRQi+++CKLFi2ibsDxRPO6HnDdnNLF+GrL8dWWk7f2OXJKE/dHBAcch8vvxv/dcmu7G4Ynkm7Dhg3DAVtrvf3Q3lyXzbBhwz09ZjIoLFJk165d3HHnnUSL+tLQJ3HzU1ZtBRZpwCIN+Ku2kVVbkfggWX5qh5zKnj27ufvuu5NQtUjn0dhnsNPDfotwFHbXQb9+7e8u0QqLFLnrrruorw9RO+SUNk3r0VbRgp4E+47hxRdf5O23307ZcUQ6mp49ewKwJ+Tdx+DeUBau2bHbE4VFCixfvpzXXnuN+r5jcLnFKT9eqN+xkFvEH6dO1ay0Iq3UeF97n6X2pkfNNR6r8djticIiyZxz/O1vf4ecQkL9xnhzUJ+fugHj2VRayiuvvOLNMUXauYqKWFNvns+7sMiNH2v37t2eHTNZFBZJtmzZMtatW0t9v2Mgy5N7SwEQ7jYYV9CDh6b9s11+axHxWuN0/4d18+5sPNcPQ7tEWLKk/Y1gVFgk2VNPPYVl59HQY4S3Bzajvs9RbN2ymaVLl3p7bJF2JhgM8tzsWQztEqF7jrdfro7rEWTNmjWsXbvW0+MeKoVFEpWXl7Nw4ULqu4+ALO/HUIe7DcECubrnhUgCDz30EJu3bOX8Yd7PgPDlgfV0zYHf33pLu+pjVFgk0dy5c3HOEe45Mj0FZPkIdhvKggULdd2FyH4899xzPPLIw5zar56junt3l7xG+X7HpSMr+ejjDdx0003tJjAUFkninOPfTz9NtLB3wgvwUqmh50jC4YbYrVtF5FOefvppfv/73zO6WwOXjkrfvGrH92rgohE1zJs3j//57/8mFAqlrZbWUlgkyfz589lcVkaw12FprSNa0JNoYW8efXRm05z9Ip1dbW0tv//975k6dSrH9QzxwzGVabmXRXNnDarnklE1vLFgAd/73jVs3LgxvQUloLBIgr179zL1T3/CFfQg3D39l/HXl4xjx47t3HvvvekuRSTt1q1bx1X/+R2ef+45zhlcy/ePqiI7Q6ZlOqOknh8dXcm20g+56j+/w6xZs3DOu6G8baGwOETl5eX84Prr2bO3ktrBp0BW+v+TRor6Eup9BE888QQPPPBAxv7jE0ml2tpa7r77bq65+mpqyrdww3F7OX94Hf70/4l+ynE9G/jd+ApGFNZy++2386Mf/jAjzzK8uxDUEI87AAAQPElEQVSgA1q5ciW/u/lmduwsp3bkGUQLMucS/uCgE7BomIceeojt27dz7bXXtrubrYgcDOccr732GnfdeQcVFbs5fUA95w+rpSCQuV+auuU4/uuYSuZtyWHme+9w5RVX8M0LL+SSSy4hNzc33eUBCouDUllZyd/+9rfYENXcImpGfYVoYe9D22kkRG5uLmeffTazZs2iOnKIHV6WRf2QCUQD+bzw4ossXLSY63/wfb74xS9iKZyrSiSdNm7cyJ133MGy5csZXBTluuOrGF7cTkYbGXxxQJBxvUI8sj6f6dOn88rLL3Hd93/AhAkT0v53q7Bog8rKSp544glmPvYYdXV1hPqOIdj/WPAd+g1ULBzi7Mlnc9111+GcY+azLx56wWaESo4n3G0I0Y0L+O1vf8uMRx/l8ssu46STTkr7Pz6RZKmrq2PatGk8NnMm2VkRvjWyhokD6vFlWJNTa3TJdnz3yBo+3y/ItA+i/PrXv2b8+HFcf/0PKSkpSVtdCotW2L17N48//jhPPPkk9XV1NHQdTGjocUTzuyftGM6f3dS5NXv2bJw/eTdzjxb0oOaIswns+oAPSlfyi1/8guHDR3DppZdw6qmnkpUB/SwiB2vlypXc8n83s2XrNk7tW88FI2opzs7cJqfWOrxbmP8dt5tXNufy1IqlXHnF5Vz13av52te+lpa/WesonZ/jxo1zyZ7mYtu2bTz66KPMmj2bhlCIhm5DCfU/Jqkh0Shv7XP4q7Y1PQ8X9aXu8ElJPw7RKP6KD8nb9g7UVTKgpIRvXXwxX/rSlwgEvL/FpMjBCoVC3Hfffcyc+Sg98xz/eVglh3swz9PNy7uwds8nfyuHd23gF2MrU3rM3UHj/rVFvFMe4NhjjuGGn/+cvn37JmXfZrbMOTcu0Xo6s2jB3r17uffee5k9+zmiztHQYzihvmPSerFd0mRlEe45kqoew/FXbKBs+7vceuut3Hvf/Vxz9XeZOHGimqck4wWDQW688de8+eYSTu9fz4UjasjrwJ9m3XIcPz66kvlbc5i++h2u/d41/OmOOxk4cKBnNWRs+4OZnWlm68xsvZnd4MUxo9Eos2fP5uJvfYtnZ82ivucoqsdMoX7oqR0jKJqzLMI9hlF9xGRqR57BzjrHTTfdxA9/9KOMHLYn0igYDPLrX/2KN99cwuWHVXP54R07KBqZwef7B7lx7B4aanbzwx98n9LSUs+On5FhYWY+4M/AWcCRwEVmdmSqj/uvf/2L2267jUryqTnyXIKDT8LlFKb6sOllRqTrQGqOOJv6wSezctUarrrqu2zdujXdlYm06JlnnmHJW29xxeHVnD4gmO5yPFdSGOHnx+6hoXYPf7j9ds+Om5FhAXwOWO+c+8g5FwJmAOem8oBbt25l2rR/0tBtKDWHTUpJv0RGsywaeh9O1ZGTCYXDuqe3ZKyXX3qRoV0ifKF/eoKiLmzk5uYyZcoUcnNzqQt732w7oCDCl/rXsvLdlezYscOTY2ZqWAwANjV7XhZf9ilmdpWZLTWzpTt37jykA37wwQeEww2x5qZO3Gbv/LlEA/msWv2ervyWjBMOh1m//kOGF3k/W2yj2rBx9tmxYe5f/epXqU1DWAAM7xLGudh0Jl7I1LBo6b/+Zz65nHN/d86Nc86N69Wr1yEd8NRTT+X0008nZ8vbZJctwxo62SR8zuGr2kb++y/hC1Vz469/pY5uyTh+v58TTjiBt3blEk7TDSHz/Y5Zs2Zx1113MXv2bPL96flStWh7Nrk5ORx33HGeHC9Tu4XKgObd/CXAllQe0Mz42c9+RiQSYf78+eRuX02wx3BCfY7C5RWn8tAARPO7E97nuSdcFP/ujeRsX0VW9U4Ki4q47qc/5fjjj/fm+CJt9I0pU1i0eDF3ryrimtFVns8ee1jXBthTzfzZM+njd7HnHntxUy4LtuVy7nlnUVjoTb9qRl5nYWZ+4H1gIrAZeAv4D+fc6v1tk8zrLEpLS5k5cyYvvPAi4XADrqAHoeJBhLsNJprXrf03U0XC+Cs349+9kezKMlxDPX379ePCb36TM888M2PmohHZnyeffJK777qLwUVhrh9T6fmtUdMlHIVH1ufzclkep06YwC9/9atD/ntt7XUWGRkWAGY2CfgT4APud8797kDrp+KivIqKCl5++WXmz3+d995bHWvDz+1CqHgg4a4DiRT2ScvtUw+GNdTi27sZ/+5Ssqs24yJh8gsKOOXkk/nCF77AiSeeiM/XPt6LCMDChQv5zW/+B4s0cPagGs4cWJcxU48nm3OwojzAI+uL2FZrXHDBBXz3u99Nyt9suw+LtkpFWDRXUVHBwoULef3111m6bBmRcBjzBQgV9SNSXEK4eAAuJ4NmdY1G8dXswLe3jOzKzVhNOQDde/TgtFNPZcKECRx77LH4/ZnaEimSWFlZGX+95x7eWLCAHnlwwdAqTugTIqudn/w3t7HKxyMfFvJehZ+BJQP43rXXcdJJJyVt/wqLFKqtreXtt99myZIlLFy0mJ07tsdeyOtKqLiEcNdBRAp7g3k8fiBcj39PGf49pWRXbcGFQ2T5fIwePZoTTziBz33uc4wYMUId19LhvP322/z57rtY/+FH9C1wTBpYwyl9gwQydQhPAs7Buj1+ZpXms7I8QFFhAZdfcSWTJ09O+hc8hYVHnHNs2rSJJUuWsGjRIlasWEEkEsECuYS6DCDcdRDh4pKkzEzbEqvfi393KYG9m/BVbwfn6NqtO6ecfBInnHACY8eO9awDTCSdIpEIr7/+Og9P/xfvf7CerrnwlQE1nD4gmLYRS20VdbB8VzazS/P5cK+PrsVdmHL+BZx77rkpux+NwiJNampqeOutt1i4cCELFi6iproq1lzVdRANPUcRKep76B3k4SCBio/ILl9PVnXs+pKhw4Zx6oQJnHzyyYwaNUozyUqn5Zxj2bJlPPzwdJYvf5tcP5zat44vl9TTJz8zO8Lrwsb8rTm8vDmfHbVGv759uPCi/+DMM88kJycnpcdWWGSAcDjMqlWrmDNnDq/MmUNdbS3kFhHsPpyG3kfiAm0bxeCr2kZg+xoCe0shGmHIkKFMmnQWp512WtJmoBTpSN5//30ee+wxXp07l0gkwnE9Q5w5sI7DuoYzYlDjzrosXirLZf62POoa4KjRRzLl/AuYMGGCZ/2JCosMEwwGeeONN3j+hRdYunQp5gtQ33s0ob5HJWyiyqopJ2fzMvx7yygsKuIrX/4yZ555pvofRFpp165dPP300zzz9L/ZW1nFsC4RJg2qZVyv9HSGb6jy8VxpHkt25GBZWXzhC6czZcoUjjjiCM9rUVhksA0bNnDvvffyxhtvYIFcotkH6FNwUay2goKCQr71rYv52te+pusgRA5SMBjkxRdf5NEZj7B5y1Z65zvOKqnh1H7BlA+7dQ5W7w4wuzSP1RUB8vNyOWfyuXzjG9+gd+9DvC3zIVBYtANr1qzhiSeeoKam5oDrjRo1iilTpqSsg0uks4lEIixYsICHH57O2rXr6JEH5w2uZkLfYEpuxfr+Hj+Pf1TA2j1+enTrypQLvsk555yTEYNPFBYiIgk451i+fDn3/uPvrFm7jr4Fjq8PqeaE3qGk9GmUVvt47MMC3ikP0K1rMZdc+m3OPvtssrOzD33nSaKwEBFpJeccCxcu5L5//IOPNmzgqO4NXH5YNb3yDm70VCgCT32cz/Ob8igoKOCi/4g1Iefl5SW58kOnsBARaaNoNMozzzzD3/56D9GGIN8YWsOXB9a3qRN87W4/963rwvZaY9KkSVx99dV06dIldUUfIt2DW0SkjbKysjjvvPM4+eST+eMf/8DDi9/kg70BrjqydbPbvlKWwz8/KKRvnz788aafMXbs2NQX7RGdWYiItMA5x2OPPcY99/yFoUURzhpYe8B+jPd2B5i7OZeTTjyRX994I/n5+d4Vewh0ZiEicgjMYrO7DhgwgN/+7//y59WJTy3OP/98rr766g45g7PCQkTkAE455RQenTmTioqKA66Xm5tLv379PKrKewoLEZEEiouLKS5O/R0zM5lmmxMRkYQUFiIikpDCQkREElJYiIhIQgoLERFJSGEhIiIJKSxERCShDjPdh5ntBDamu44OpCewK91FiLRA/zaTa7BzrleilTpMWEhymdnS1swXI+I1/dtMDzVDiYhIQgoLERFJSGEh+/P3dBcgsh/6t5kG6rMQEZGEdGYhIiIJKSzkU8zsTDNbZ2brzeyGdNcj0sjM7jezHWa2Kt21dEYKC2liZj7gz8BZwJHARWZ2ZHqrEmnyIHBmuovorBQW0tzngPXOuY+ccyFgBnBummsSAcA5Nx848O3qJGUUFtLcAGBTs+dl8WUi0skpLKQ5a2GZhsuJiMJCPqUMGNjseQmwJU21iEgGUVhIc28BI81sqJllAxcCz6S5JhHJAAoLaeKcCwPXAS8Ca4CZzrnV6a1KJMbMHgEWAYeZWZmZXZnumjoTXcEtIiIJ6cxCREQSUliIiEhCCgsREUlIYSEiIgkpLEREJCGFhYiIJKSwkE7PzC4zs3fNrNbMtpnZPWbWtZXbbjCzL6W6RpF0U1hIp2ZmPwFuBf4LKAZOBAYDL8evYhcRFBbSiZlZF+A3wPedcy845xqccxuAC4gFxrfM7EEzu6nZNl8ws7L4438Cg4BnzazazH4aXz7BzBaa2R4z22Rml8WXF5vZNDPbaWYbzexXZpYVf+0yM1tgZlPj231kZifHl2+K3/Tn283qyDGz282s1My2m9lfzSzPk/9w0ikpLKQzOxnIBZ5svtA5Vw08D5xxoI2dc5cApcA5zrlC59zvzWxQfNu7gF7AscCK+CZ3ETt7GQZ8HrgUuLzZLk8AVgI9gIeJ3U9kPDAC+BZwt5kVxte9FRgV3/8IYlPJ39i2ty/SegoL6cx6Arvic2Lta2v89ba6GHjFOfdI/Eyl3Dm3In4Xwm8CP3fOVcXPYP4AXNJs24+dcw845yLAo8RmAP5f51zQOfcSEAJGmJkB/wn8yDlX4ZyrAm4mNvGjSEr4012ASBrtAnqamb+FwOgXf72tBgIftrC8J5ANbGy2bCOfvrnU9maP6wCcc/suKyR2xpIPLIvlBhC7F4nvIOoVaRWdWUhntggIAl9vvtDMCojdh3wOUEPsg7lR3332se9MnJuA4S0caxfQQKwvpNEgYHObq47tqw4Y7ZzrGv8pds4VJtpQ5GApLKTTcs7tJdbBfZeZnWlmATMbAjxG7EZQ/yTW3zDJzLqbWV/gh/vsZjuxPohG04EvmdkFZuY3sx5mdmy8aWkm8DszKzKzwcCPgX8dRN1R4B/AVDPrDWBmA8zsK23dl0hrKSykU3PO/R74BXA7UAm8SezsYKJzLkgsMN4BNgAvEetLaO7/gF/FRzD9P+dcKTAJ+AlQQSxsjomv+31iZyofAW8Q68S+/yBL/xmwHlhsZpXAK8BhB7kvkYR0PwsREUlIZxYiIpKQwkJERBJSWIiISEIKCxERSUhhISIiCSksREQkIYWFiIgkpLAQEZGEFBYiIpLQ/wenN6UjYFjBWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome',y='Insulin',data=train,hue='Outcome')\n",
    "plt.xlabel('Outcome' ,fontsize=12)\n",
    "plt.ylabel('Insulin',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/listen/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'COUNT')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BMI体重指数\n",
    "fig=plt.figure()\n",
    "sns.distplot(train.BMI,kde=False)\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('COUNT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/listen/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "sns.violinplot(x='Outcome',y='BMI',data=train,hue='Outcome')\n",
    "plt.xlabel('Outcome')\n",
    "plt.ylabel('BMI')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/listen/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'COUNT')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#DiabetesPedigreeFunction: 糖尿病家族史\n",
    "fig=plt.figure()\n",
    "sns.distplot(train.DiabetesPedigreeFunction,kde=True)\n",
    "plt.xlabel('DiabetesPedigreeFunction')\n",
    "plt.ylabel('COUNT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1317775198>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['DiabetesPedigreeFunction', 'Outcome'])['DiabetesPedigreeFunction'].count().unstack('Outcome').fillna(0)\n",
    "DF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/listen/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'count')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Age\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Age, kde = False)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1316a8bdd8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['Age', 'Outcome'])['Age'].count().unstack('Outcome').fillna(0)\n",
    "DF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1316ae3c50>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#看各个特征的相关性\n",
    "data_corr=train.corr().abs()\n",
    "plt.subplots(figsize=(10,10))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "#data_corr = train.corr().abs()\n",
    "#plt.subplots(figsize=(13, 9))\n",
    "#sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for feature in train.columns:\n",
    "  #  sns.distplot(train[feature],kde = False)\n",
    "  #  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
